GPT-4-Powered Medical Recommendation System using SNOMED

The adoption of generative AI in healthcare has significantly transformed medical recommendation systems, providing tailored, accurate, and context-sensitive support for patients and clinicians. This research emphasizes the impact of SNOMED based GPT-4, an advanced language model, in creating a dependable system for medical recommendations. Trained on the Keivalya/MedQuad-MedicalQnADataset, SNOMED based GPT-4exhibits remarkable performance in generating precise, contextually relevant, and safe medical responses essential for practical applications. It achieves a low perplexity score of 10.2, reflecting its ability to deliver fluent and coherent recommendations, while maintaining a minimal harmfulness rate of 1.2%, ensuring user safety and reliability. These attributes position it as an ideal candidate for powering systems that assist with tasks like symptom assessment, disease management, and patient education. The significance of such systems lies in their ability to enhance access to medical expertise, reduce delays in providing critical advice, and support healthcare professionals in managing high workloads. By accurately interpreting medical queries and offering actionable suggestions, this contributes to better health outcomes and reinforces confidence in AIdriven healthcare tools. This study highlights the growing need for reliable and secure recommendation systems within healthcare. With its exceptional performance, SNOMED based GPT-4 serves as a vital technology for bridging gaps in medical accessibility and improving patient-provider interactions.